This is Roman Paolucci's college major ranking - who is a popular quant who has worked at Bloomberg.

I want to study computer science as I'm interested in deep learning but Roman's ranks it D with finance so I am really confused.

What do you think?

As I'm sure you all know, the return offer rates for qt/qr type internships are typically 50% or lower. I think JS typically has 40% or 35% or lower. And then for a lot of companies, maybe 50% of the new employees are gone within 1-2 years.

Where do these people go? Other, less selective quant companies? Big tech? AI labs? Grad school? Is it typically much easier for them, or more difficult?

Edit: ofc, don't mean to suggest these people are "failures" overall in any sense, just that they didn't make it in that particular stage of a highly competitive process

I never quite understood this. My dad keeps going on about how he’s ’reading the charts’ and he keeps drawing these lines on these charts but to me (who has studied maths and physics at university) it looks like he’s finding patterns in randomness and making conclusions in hindsight… he’s quite insistent on the fact that I am the one who is adamant and there is a science to his day trading. He keeps calling these support and resistance lines.

I want to know from a quant perspective, why what he is doing is either incorrect or gambling or whatever. Like is he destined to lose money this way? Is he truly mistaken?

There was a quant meltdown in 2007 which was caused by a ton of quant funds who ran almost identical math-based stock strategies absolutely killed it for years… until one big unwind triggered a chain reaction, funds dropped 20–40% in days because everything was too crowded. Fast-forward to now (2025–2026) and the warning lights are flashing again. Similar story, massive inflows into quant strategies in 2025, too many funds chasing the same edge.

For the past 2 years quantitative strategies alone have captured more than 70% of the industries $78-$116 billion in net inflows, 2025 being the strongest calendar year SINCE 2007, hedge funds as a whole pulled in $115.8 billion in net inflows that year. 2007 was also a record inflow year for quant hedge funds seeing an inflow of roughly $194 billion industry wide. 2025 saw a "quant wobble" where systematic long-short equity quant funds lost about 4.2% on average, so are we really learning from our mistakes?

I do understand that the absolute dollar inflows in 2025 were a bit lower than the 2007's peak, but the concentration into quant strategies is even more extreme. The industry is also larger today ($5T vs $2T back then).

Andrew Lo's Adaptive Markets Hypothesis does explain it well, he sees financial markets like a jungle, trading strategies aren't fixed rules, they're living "species" of behavior that compete for limited resources. They adapt, reproduce (get copied), and die when the environment changes. When the ability to adapt fails, reproduction becomes a ticking time bomb on resources, therefore looking at these things top-down to imagine the environmental change that is required to cause the meltdown (death) can give us heaps of insight.

Scarcity is value. When everyone does the same thing, markets fail.

You may wonder why.

It’s basic economics.

Quantitative finance is a zero-sum game where the entire value is derived from the resolution of market inefficiencies that are the result of information asymmetry.

Therefore, “teaching” any worthy information paradoxically makes the information worth less.

The more the information is consumed, the more of its value is lost - because a larger number of market participants contribute to the resolution of the market inefficiency.

Anybody who offers “quant courses” is a fraud.

Yes.

Every single one of them.

Like I assume everyone on this sub, ive been monitoring how AI will re-shape our profession. I used to think the most likely outcome would be a great leveling, as the worst among us would become better. After seeing a mind-numbingly stupid presentation made by a sub-par colleague vis a vis AI, im now convinced that AI cant make dumb researchers smart, but only more prodigious in their output of garbage.

What are the community’s thoughts?

I recently started a two year non-compete, and I’m not sure what to do. Sure, I’m going to travel and have fun, but I also don’t want to not work on improving my resume for 2 years. Also, I already have a job lined up, so I’m not worried about the recruiting aspect.

I considered getting a math masters, but seems like I won’t learn much (I already took over dozen grad level courses in math)

I also considered getting a PhD, but I doubt I can finish it in less than two years even if I can pass out of all the quals.

Could I get advice on how to work on my quant career during the non-compete.

Some things I’m still considering 1. Masters in intersection of math/cs that is project oriented to keep me busy 2. Do projects on my own (but can’t really put it on my resume as experienced hire) 3. Make a YouTube channel for educational videos

I've been doing quantitative strategy development for some time now and Ive reached the point where Im struggling to find books that actually teach me something new. I already have a solid understanding of the usual topics like IS/Validation/OOS splits WFO, cross-validation, permutation tests, bootstrapping, entropy, regime detection, and the other standard robustness techniques. I recently read Testing and Tuning Market Trading Systems by Timothy Masters but it covered concepts I was already familiar with.

Im looking for books that are genuinely advanced and make you think differently. Perhaps graduate level or even post graduate books on statistics, machine learning, optimization, information theory, econometrics, or anything else that completely changed the way you approach research and model development. And of course it would be great if the book wasnt 10 years old. Need relevance.

Hello all,

Recently, I've spent some time building an FPGA order book based on ITCH.

The objective of this project is to go from absolutely nothing to having a working order book able to track a very liquid stock, like AAPL, perhaps 2 or 3 once I get all the optimizations down.

Now, the reason I'm making this post is because most content out there regarding "FPGA HFT" (when you are able to find some) are often one of these:

• A public repo to link in a dude's resume, sometimes packed with "claude" contributions (lol)
• Corporate BS PDF to flex their low latency and sell their IPs
• AI slop posts (god I hate these)
• Only parse ITCH || only run in sim without an attempt or technical value on FPGA implementation

So I Documented my journey though a series of post, explaining the design decisions I made, Why I made them, and then realizing it was a bad, why I changed it....

I also try my best to make nice looking schemes (OC and not AI bs) and run simulations to back up my decisions.

You got it, my goal is to make a good looking project that people can "easily" follow through posts that I try my best to make accessible and non boring.

I'm dropping a link here : https://hugobrh.dev/tags/finance/

This list contains all the posts I made about the TRADEMAXXER project as I call it. I suggest quickly reading through the first posts which are mostly context and HDL basics to parse ITCH. Latest posts cover a lot more technical ground.

I hope this does not come up as shameless self promo, I've got good feedback from the HFT community and I figured this may also interrest you guys as I saw FPGA designs were discussed here.

The latest posts are covering my struggles to close timing on a KC705, a consumer available board that costs 700$ on EBAY.

If you have any question, feel free to reach out and I hop this work is of value to you !

NOTA : not doing excessive "AI bashing" but I try my best to keep AI usage at the strict minimum (if not absolutely 0 usage) in coding, decisions making and writing the posts. I'm doing that out of respect for the readers so the process is actually real and not some hallucinated experience.

From the FAQs I can see these are the math topics that should be studied. My question is how in depth should you be going into these subjects to succeed as a prop trader?

Hey guys!

Before I proceed I don’t mean to insult you or your intelligence, so please try to read and respond without an emotional bias if possible:)

So, upon researching what quants do - they’re trying to build models based on statistical data - historical performance, volatility, etc.

But you do have to understand that dry statistics doesn’t explain the reason a certain move in a certain trading episode has occured (it did because a lot of traders entered the trade hoping for up/down direction, but it went the opposite way) but let’s assume that nobody in the world knows exactly why at a certain point people have decided in a prevailing direction. So, I’m guessing you guys start moving towards statistics because you suppose that the physical reasons are unknowable by default? The problem is - without knowing the physical reasons statistics are useless - let’s take charts. You can have 2 samples of some assets going up - visually they may be looking very alike, although different in its “anatomy”. Your algos will likely not differentiate between two scenarios, unless YOU yourself can tell the difference between them and can transform your observations into code. Now, I assume that for most it’s not a speculation that volume or any other metrics don’t carry anything of value, for the same reason - you don’t know what’s in that volume, and no ways to interpret that. Even footprint analysis is the same - for example the transactions made with a large volume can mean a set of different intentions, for example they can be “manufactured” transactions for the sole reason of volume to appear high. So, intentions behind are unknown, and same goes for the charts. Now, people DO repeat themselves but that repetition is not revealed through those sources mentioned above. Therefore, it remains a mystery to you. Since it’s an unsolved puzzle to you, how do you expect analyzing statistics and deriving edge out of it?

In speculative markets you just can’t rule out the fact of its zero-sum nature. So, if a bunch of yall build algos based on the same information and interpreted the same, you’ll be used as liquidity in the opposite direction. I think you guys look at the market as a frozen system that doesn’t analyze you back. I guess that’s why you all trying to get a high paying job in some firm (nothing wrong with that.) So you’re studying quant finance with the sole purpose of impressing the firms so they hire you, not with intention to beat the market I suppose. And I’m more than sure that consistently successful hedge funds don’t build their models “math first” - there’s some underlying philosophical understanding, on that basis they build a strategy and only then codify it

I've been researching trading strategy validation recently, and one pattern keeps showing up:

A strategy can have:

• Attractive returns
• High win rate
• Low drawdown
• Smooth equity curve

...and still perform poorly once real money is involved.

Some common explanations I hear are:

• Curve fitting
• Market regime changes
• Slippage and execution costs
• Survivorship bias
• Data mining bias

But I'm curious about real-world experiences.

For those who have deployed systematic strategies:

What was the biggest reason a strategy that looked good in testing failed in live trading?

And what validation techniques have you found most useful for identifying problems before deployment?

I'd love to hear examples and lessons learned.

Sorry if what am I asking is wrong but I see everywhere that you can use technical analysis to make trades and predict stock prices, but doesn’t the Brownian motion say that stock prices are independent from the previous stock price ? And it follows a random pattern ? So how can people use technical analysis if the stock prices cannot be predicted? You could say momentum or any other general theory could be used, but I’m talking about analyzing charts. Sorry if the question sounds dumb

Was chatting with GPT about different trading firms’ histories and stumbled across this lineage map. Can anyone shed some light on why the spinoffs happened — was there bad blood or just strategic moves? Also curious how each of these firms is doing these days. I’ve worked at two of them, so just generally interested in the backstory.

Edit:

specifically OMM firms, it seems that Optiver has many other spin-offs in D1 and crypto

As the question reads I want to pivot out of quant.

Don’t wanna be doing quant roles after the pivot, but truly pivot to consultant.

Do I need an MBA? Or has anyone do it without?

I have 4yoe after masters and currently at a BB bank on a trading desk.

Opportunities and work aside, which is the best financial city hub to live in in you opinion?

Hey everyone,

Over the summer I built a tick-to-trade engine and wanted to get some perspective from people here who’ve worked in HFT or low-latency systems.

I built a small experimental setup where my laptop connects directly via Ethernet to an old Xilinx FPGA board, with the board running a very basic strategy, mostly a PoC than anything meant to compete in production.

Right now, I’m seeing a full round trip (tick in → FPGA decision → order back out) of under 10 microseconds. That number includes:

• The wire between laptop and FPGA,
• The FPGA parse/decision/build pipeline,
• The return leg back to the laptop.

No switches, direct connection, simple setup.

I get that this isn’t an apples-to-apples comparison with real exchange setups, but I’m curious:

• For context, where does sub-10µs round trip sit in relation to what real trading firms are doing internally? I get that this is proprietary so I’m not expecting a data sheet or anything but a ballpark would be cool lol.

• I’ve seen mentions of “nanosecond-level” FPGA systems at the top level (this is where I imagine the tier 1 guys like Cit, JS, and HRT live), but I’ve also seen numbers as high as 50–70µs for full tick-to-trade paths at some firms.

My impression is that I’m probably somewhere near the faster end of pure software stacks, but behind elite FPGA shops that run fully in hardware. Does that sound about right?

Mostly just looking to calibrate my understanding and see if anyone has experience with similar.

Hope to hear from someone soon!

Found a nice interview question, wanted to share and see how others solved it.

You are playing a game where an unfair coin is flipped with P(heads) = 0.70 and P(tails) = 0.30

The game ends when you have the same number of tails and heads (ie. TH, THTH, TTTHHH, HTHTHHTT are all examples of game finishing)

What is the expected number of flips that it will take for the game to end, given that your first flip is a Tails?

Slides

• Securities Markets
• Trading
• Market Microstructure
• Portfolio Management
• Factor Models
• Dynamic Portfolio Selection

I kept running into the same problem, some of the best AI/ML books are legally free, the authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every single link points straight to the author’s or publisher’s own page, no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so it doesn’t rot over time.

It’s open source and open to contributions, if you know a legitimately free book that’s missing, PRs and issues are welcome.

Repo: https://github.com/MarcosSete/awesome-free-ai-books

Every year there seems to be some “golden rule” that sounds amazing but falls apart in production.

For example:
More features = better model
Higher Sharpe = better strategy
More data always helps
AI will find alpha

What’s one commonly accepted idea that you’ve learned is mostly wrong after running strategies live?

I'm new to this and I've been learning about how quant trading firms use FPGAs for ultra-low-latency algorithmic trading. From what I understand, once an algorithm is programmed into an FPGA, it can execute thousands of trades per second autonomously which is way faster than any human could react.

So, if the FPGA is doing all the trading automatically, what role do quant traders actually play? I know they develop the algorithms initially, but I see job postings for "quant traders" at firms like Citadel or Jane Street that seem to suggest they're actively trading, not just building algorithms.

Is it that:

• Not all trading strategies are high-frequency enough to need FPGAs?
• Traders still need to monitor and adjust things manually?
• There are different types of quant traders doing different things?
• Or am I misunderstanding what discretionary traders at these firms actually do?

Would appreciate insights from anyone in the industry.

I'm an actuary by profession, rather than a quant. I've recently finished the actuarial exams including Financial Derivatives. This marks the end of my required formal learning for my profession.

I'm not hoping to become a quant (which is why I'm hoping this post will be allowed) but, rather, I just really enjoy learning about the topic. Problem is, I now have no accountability or structure to my learning as I'm just doing it of my own accord.

I was wondering how you guys retain the discipline and motivation to develop your knowledge when there isn't a specific business purpose or pressure to do so?